Resolving the effect of wrist position on myoelectric pattern recognition control

Background: The use of pattern recognition-based methods to control myoelectric upper-limb prostheses has been well studied in individuals with high-level amputations but few studies have demonstrated that it is suitable for partial-hand amputees, who often possess a functional wrist. This study's objective was to evaluate strategies that allow partial-hand amputees to control a prosthetic hand while allowing retain wrist function. Methods: EMG data was recorded from the extrinsic and intrinsic hand muscles of six non-amputees and two partial-hand amputees while they performed 4 hand motions in 13 different wrist positions. The performance of 4 classification schemes using EMG data alone and EMG data combined with wrist positional information was evaluated. Using recorded wrist positional data, the relationship between EMG features and wrist position was modeled and used to develop a wrist position-independent classification scheme. Results: A multi-layer perceptron artificial neural network classifier was better able to discriminate four hand motion classes in 13 wrist positions than a linear discriminant analysis classifier (p = 0.006), quadratic discriminant analysis classifier (p < 0.0001) and a linear perceptron artificial neural network classifier (p = 0.04). The addition of wrist position data to EMG data significantly improved performance (p < 0.001). Training the classifier with the combination of extrinsic and intrinsic muscle EMG data performed significantly better than using intrinsic (p < 0.0001) or extrinsic muscle EMG data alone (p < 0.0001), and training with intrinsic muscle EMG data performed significantly better than extrinsic muscle EMG data alone (p < 0.001). The same trends were observed for amputees, except training with intrinsic muscle EMG data, on average, performed worse than the extrinsic muscle EMG data. We propose a wrist position-independent controller that simulates data from multiple wrist positions and is able to significantly improve performance by 48-74% (p < 0.05) for non-amputees and by 45-66% for partial-hand amputees, compared to a classifier trained only with data from a neutral wrist position and tested with data from multiple positions. Conclusions: Sensor fusion (using EMG and wrist position information), non-linear artificial neural networks, combining EMG data across multiple muscle sources, and simulating data from different wrist positions are effective strategies for mitigating the wrist position effect and improving classification performance.